About

Lauren Stadler is an Associate Professor of Civil and Environmental Engineering at Rice University. She brings 13 years of leadership experience in civil and environmental engineering with a focus on reimagining wastewater systems as valuable sources of information for protecting public health. Her research centers on wastewater-based epidemiology, environmental antibiotic resistance, wastewater treatment and resource recovery, and environmental synthetic biology. Since joining Rice University in 2016 as an assistant professor, Stadler has collaborated extensively to develop methods for detecting viral variants, antibiotic resistance genes, and bacteria in wastewater. In May 2020, she began working with the Houston Health Department and Houston Public Works to establish protocols for wastewater initiatives aimed at tracking community COVID-19 infection dynamics. Her laboratory has developed highly sensitive assays to detect low concentrations of viral loads in wastewater. Stadler's work has been recognized through numerous awards, including the Centers for Disease Control and Prevention National Wastewater Surveillance System Center of Excellence Award in 2022, and she has published over 30 peer-reviewed journal articles. Her educational background includes a Ph.D. in Environmental Engineering from the University of Michigan, along with a master's and bachelor's degree from the same institution and Swarthmore College, respectively.

Research topics

• Computer Science
• Medicine
• Biology
• Environmental engineering
• Environmental science
• Engineering
• Virology
• Business
• Biochemical engineering
• Sociology
• Environmental planning
• Political Science
• Ecology
• Computational biology
• Internal medicine
• Microbiology
• Environmental health
• Computer network
• Operations management
• Data science
• Veterinary medicine
• Nanotechnology
• Telecommunications
• Public relations

Selected publications

• Cross-order detection of bacteriophage transduction in microbial communities using RNA barcoding

  Nature Communications · 2026-03-23 · 1 citations

  articleOpen accessSenior author

  Bacteriophages (phages) facilitate gene transfer and microbial evolution in all ecosystems and have applications as tools for engineering microbiomes and as antimicrobials. Historic efforts to map phage hosts, such as plaque assays, are limited to cultured bacteria, are low throughput, and are hard to apply in microbial communities and environmentally-relevant contexts. To overcome these limitations, we integrate a synthetic ribozyme that stores information about participation in horizontal gene transfer in 16S ribosomal RNA (rRNA) into the phage-plasmid P1, and perform targeted 16S rRNA sequencing following transduction to identify phage-host interactions. Experiments in synthetic and wastewater communities reveal Aeromonadales as a previously unreported P1 host order and show P1 transduction into pathogens. In wastewater, host range varies across phagemids having different origins of replication and phage-derived particles having different tail fibers. This work shows how autonomous barcoding can be used in phages to identify the molecular controls on their host range in microbial communities. Bacteriophages are the most abundant life form on earth and can be applied to eliminate or engineer bacteria. Here, authors demonstrate RNA barcoding as a high throughput tool to measure bacteriophage host range in natural microbial communities and inform bacteriophage ecology and applications.

  PublisherOA PDFDOI

• Real-Time Bioelectronic Sensors based on Electroactive Bacteria with Organic Electrochemical Transistors

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Assessing Resistome Host Range Across Water Reclamation in Three Geographically Distinct Communities using Hi-C Sequencing

  medRxiv · 2026-02-16

  articleOpen access

  Antimicrobial resistance (AMR) is a growing problem, with annual deaths set to pass 10 million by 2050 if current trends continue. Wastewater surveillance has been proposed as a strategy to understand population-level resistance, and water reclamation facilities (WRFs) have been identified as a control point for environmental dissemination of resistant bacteria. Understanding dynamics of AMR across WRFs requires advanced molecular tools that elucidate host bacteria, especially for mobile resistance carried on plasmids. To that end, influent, activated sludge, and effluent were collected from three WRFs in North Carolina, Texas, and California during three weeks of Spring 2024. Samples were analyzed using Hi-C proximity ligation sequencing to identify the AMR host range for chromosomal and plasmid-based resistance. A total of 1,868 hits for 244 unique resistance genes were observed, with seven resistance genes identified in all samples. Resistance genes were more likely to be carried on a microbial plasmid in influent, but more likely to be in a chromosome in activated sludge. Seventeen total microbial hosts for resistance genes were identified in effluent, suggesting WRF effluents may be sources of resistant bacteria to receiving surface waters. A high proportion of all identified host relationships were confined to just four bacterial families. Hi-C contact mapping is a critical tool to more fully describe the AMR host range in complex matrices, particularly for plasmid-based resistance genes.

  PublisherOA PDFDOI

• Real-time bioelectronic sensors based on electroactive bacteria with organic electrochemical transistors

  Biosensors and Bioelectronics · 2026-05-14

  article
  PublisherDOI

• Author response for "Understanding Transmission and Infections of Respiratory Syncytial Virus through Wastewater-Based Epidemiology †"

  2026-03-09

  peer-reviewSenior author
  PublisherDOI

• Tracking <i>Candida auris</i> in Communities via Wastewater: Facility-Level Surveillance and Targeted Sequencing

  medRxiv · 2026-02-02

  articleOpen accessSenior authorCorresponding

  Abstract Candida auris is a multidrug-resistant fungal pathogen that presents substantial challenges for healthcare facilities due to its high mortality rates among vulnerable populations. Six C. auris clades have been identified based on their susceptibility to antifungal treatment and environmental stressors. Identifying the circulating C. auris clade(s) is critical for understanding transmission and selecting a disease control strategy. To inform targeted implementation of community wastewater monitoring for C. auris , samples were collected over 34 weeks from 8 nursing homes and 6 downstream wastewater treatment plants (WWTPs). Detection rates and concentrations of C. auris DNA were significantly higher in samples from nursing homes compared to those from WWTPs. Amplicon sequencing methods were developed and applied to characterize the circulating C. auris clade in a nursing home wastewater sample. This study demonstrates the utility of wastewater monitoring as a resource-efficient approach for detecting and subtyping C. auris in vulnerable communities.

  PublisherOA PDFDOI

• City of Houston Respiratory Syncytial Virus Wastewater Monitoring and Health Care Encounter Data

  Kinder Institute Urban Data Platform · 2026-03-13

  datasetOpen access

  RSV wastewater and health encounter data for the City of Houston from January 2023 to December 2024.

  PublisherDOI

• Measles Wild-Type Virus Detection Through Wastewater Surveillance in Sandoval County, New Mexico

  JAMA Network Open · 2026-05-06

  articleOpen accessSenior author

  This cross-sectional study identifies positive detections of measles wild-type virus in wastewater in Sandoval County, New Mexico, and examines their associations with clinical cases and impacts on public health actions.

  PublisherDOI

• Quantifying Resilience and Sustainability of Different Plant Operations at a Wastewater Plant Experiencing Wet Weather

  ACS ES&T Engineering · 2026-05-18

  article

  Water resource recovery facilities (WRRFs) face growing challenges maintaining stable nutrient removal under variable wet weather conditions. This study evaluates those challenges using data from a five-stage Bardenpho plant (7.5 MGD design capacity). Dynamic process simulation was used to measure system response to hydraulic disturbances through three resilience metrics: maximum performance reduction (mpr), recovery time (tr), and recovery phase performance reduction (rppr). Life cycle assessment (LCA) was then applied to assess how different operational responses affect environmental impacts, focusing exclusively on the liquid treatment train and excluding solids treatment processes. The results showed that direct N2O emissions dominated the plant carbon footprint within this system boundary, exceeding CH4 contributions by several orders of magnitude. Wet weather prolonged recovery times to about 6 days and raised global warming and eutrophication impacts by 20–60%. Flow equalization improved resilience by shortening recovery time and lowering environmental burdens by up to 40% through more stable hydraulic loading and reduced aeration demand. Chemical addition further improved nutrient removal but created additional upstream impacts from reagent production. Overall, resilience and environmental sustainability were not always aligned; the plant could maintain stable performance (low mpr) while still generating higher indirect emissions. These findings support the development of nutrient management strategies that balance operational stability with environmental sustainability.

  PublisherDOI

• Data-Driven Framework to Quantify Nitrogen Process Resilience at Wastewater Treatment Plants

  ACS ES&T Water · 2026-01-05 · 1 citations

  article

  Wastewater treatment plants are routinely and increasingly impacted by abnormal influent flow variations, driven by extreme weather and aging infrastructure. This study introduces a data-driven framework that integrates statistical process control (SPC) with resilience assessment to quantify system response to flow anomalies. The approach was applied to two full-scale treatment facilities in the Mid-Atlantic U.S., both served by combined sewer systems. SPC, implemented using eight rule-based criteria, detected deviations in influent flow that were subsequently evaluated for effects on nitrogen process resilience. Resilience was quantified using three complementary metrics: maximum performance reduction (mpr), recovery time (tr), and recovery phase performance reduction (rppr). At Plant I, flow anomalies were associated with substantial degradation in nitrogen removal performance, suggesting gaps in rainfall representation and internal operational impacts. At Plant II, only storm-related anomalies occurred, with tr varying widely among events. The results demonstrate that influent flow deviations and effluent quality data can be leveraged to interpret process disruptions. The framework provides a scalable postevent analysis tool for utilities and underscores the need for real-time detection to improve nitrogen process resilience.

  PublisherDOI

Recent grants

• Antibiotic resistance gene propagation: in situ rates and networks of horizontal gene transfer in wastewater

  NSF · $330k · 2018–2022

• RAPID: Assessment and treatment of flood-contaminated water sources and hot-spots of microbial contaminants in post-Harvey Houston

  NSF · $200k · 2017–2019

Frequent coauthors

• Loren Hopkins

  University of California, Davis

  84 shared

• Katherine B. Ensor

  Rice University

  66 shared

• David Persse

  New York City Fire Department

  48 shared

• Kaavya Domakonda

  Houston Health and Human Services Department

  42 shared

• Catherine D. Johnson

  Houston Health and Human Services Department

  40 shared

• Komal Sheth

  Memorial Hermann

  39 shared

• Edward Septimus

  Memorial Hermann

  37 shared

• Janeana White

  Houston Health and Human Services Department

  36 shared

Awards & honors

• Centers for Disease Control and Prevention, National Wastewa…
• Gulf Research Program Early Career Fellow (2019)
• Johnson & Johnson WiSTEM2D Engineering Scholar (2018)
• CH2M/AEESP Outstanding Doctoral Dissertation Award (2016)
• Environmental Science & Technology Letters Best Paper Award…